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Ocean oil slicks can be attributed to natural seepages or to anthropogenic discharges. To date, the global picture of their distribution and relative natural and anthropogenic contributions remains unclear. Here, by analyzing 563,705 Sentinel-1 images from 2014–2019, we provide the first global map of oil slicks and a detailed inventory of static-and-persistent sources (natural seeps, platforms, and pipelines). About 90% of oil slicks were within 160 kilometers of shorelines, with 21 high-density slick belts coinciding well with shipping routes. Quantified by slick area, the proportion of anthropogenic discharges was an order of magnitude greater than natural seepages (94 versus 6%), in contrast to the previous estimate quantified by volume during 1990–1999 (54 versus 46%). Our findings reveal that the present-day anthropogenic contribution to marine oil pollution may have been substantially underestimated.

Using data collected by the Hyperspectral Imager for the Coastal Ocean (HICO) on the International Space Station between 2010–2014, hyperspectral reflectance spectra of various floating matters in global oceans and lakes are derived for the spectral range of 400–800 nm. Specifically, the entire HICO archive of 9411 scenes is first visually inspected to identify suspicious image slicks. Then, a nearest-neighbor atmospheric correction is used to derive surface reflectance of slick pixels. Finally, a spectral unmixing scheme is used to derive the reflectance spectra of floating matters. Analysis of the spectral shapes of these various floating matters (macroalgae, microalgae, organic particles, whitecaps) through the use of a spectral angle mapper (SAM) index indicates that they can mostly be distinguished from each other without the need for ancillary information. Such reflectance spectra from the consistent 90 m resolution HICO observations are expected to provide spectral endmembers to differentiate and quantify the various floating matters from existing multi-band satellite sensors and future hyperspectral satellite missions such as NASA's Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission; Geosynchronous Littoral Imaging and Monitoring Radiometer (GLIMR) mission; and Surface Biology and Geology (SBG) mission. All spectral data are available at https://doi.org/10.21232/74LvC3Kr (Hu, 2021b).

Smokes from biomass burning can contribute substantial amounts of hazardous substances and carbon to the atmosphere. These substances can be transported seaward and deposited on the ocean surface. In this study, Moderate Resolution Imaging Spectroradiometer (MODIS) images are used to map the relative smoke concentration over the ocean between November 8 and 11, 2018 from the recent California fires, with the ultimate goal of developing a generally applicable approach to map smokes over oceans. Because both biomass-burning smokes and clouds can produce strong backscattering signals, two key differences are used to separate them: 1) water-vapor absorption in certain wavelengths only occurs in clouds and 2) cumulus and cirrus clouds occur at different altitudes, therefore, bearing different thermal signatures. Based on these observations, a decision-tree method is developed to separate smokes from clouds. First, MODIS top-of-atmosphere (TOA) reflectance at 936 nm is used to detect both clouds and smokes over oceans. Then, brightness temperature derived from the 9730-nm band is used to separate cirrus from others. Finally, a water absorption depth (WAD) index is used to distinguish cumulus clouds from smokes, whose relative concentration in each image pixel is estimated from the MODIS TOA reflectance at 859 nm. Such derived smoke distribution and concentration are validated using concurrent Cloud-Aerosol Lidar and Infrared Pathfinder Satellite (CALIPSO) data, which provide the fine mode aerosol optical thickness (AOT) of smokes. Test of the approach over the recent Australia fires shows promising results, suggesting that the approach might be implemented by operational agencies to monitor and quantify smokes from biomass burning on a routine basis.

“Whiting” events in the Bahama Banks, due to high concentrations of carbonate-rich particles suspended in the water, have been reported and discussed widely in the past 80 years. However, little is known about their distributions and particularly about their long-term changes. Here, using a deep learning (DL) model, we objectively delineate and quantify whiting features from Aqua MODIS (Moderate Resolution Imaging Spectroradiometer) satellite images (250-m resolution) and establish an 18-year data record (2003−2020) of whiting occurrences in the Bahama Banks. Both the Great Bahama Bank (GBB) and the Little Bahama Bank (LBB) show clear seasonality in whiting areas, where a primary peak in spring and a secondary peak in winter are found in the GBB but only one peak in winter is found in the LBB. Such a seasonality may be explained using a hydrodynamic hypothesis on calcium carbonate precipitation. The mean size of individual whiting patches in the GBB is about 2.4 ± 6.1 km2 (∼0.1 to 226 km2), while in the LBB is 1.4 ± 2.7 km2 (∼0.1 to 95 km2). The total whiting coverage in a typical cloud-free image is 87.1 km2 in the GBB and 32.0 km2 in the LBB, representing 0.14% and 0.76% of the entire GBB and LBB, respectively. Significant increases in the mean coverage have been found in the GBB since 2011, with peak coverage (∼200 km2) in 2013–2015 being at least 4 times higher than before (20–70 km2). Although the whiting area started to decrease after 2015, it did not reach the pre-2011 level until 2020. On the other hand, correlation analysis and principal component analysis of several environmental factors (pH, light, salinity, carbonate, aragonite, winds, currents) provided some hints on which factors may have contributed. From these, we infer a potential ‘Goldilocks’ scenario, whereby decreases in pH and carbonate concentration, concomitant with increases in Sea Surface Temperature (SST) and current speeds, created conditions increasingly favorable for whitings from 2011 to 2015. Continuation of these environmental trends after 2015, however, resulted in conditions increasingly unfavorable for whiting formation, yet without field-based measurements it is difficult to conclude the potential reasons for increases and decreases of whiting formation in the GBB.

Estuarine and coastal geomorphology, biogeochemistry, water quality, and coastal food webs in river-dominated shelves of the Gulf of Mexico (GoM) are modulated by transport processes associated with river inputs, winds, waves, tides, and deep-ocean/continental shelf interactions. For instance, transport processes control the fate of river-borne sediments, which in turn affect coastal land loss. Similarly, transport of freshwater, nutrients, and carbon control the dynamics of eutrophication, hypoxia, harmful algal blooms, and coastal acidification. Further, freshwater inflow transports pesticides, herbicides, heavy metals, and oil into receiving estuaries and coastal systems. Lastly, transport processes along the continuum from the rivers and estuaries to coastal and shelf areas and adjacent open ocean (abbreviated herein as “river-estuary-shelf-ocean”) regulate the movements of organisms, including the spatial distributions of individuals and the exchange of genetic information between distinct subpopulations. The Gulf of Mexico Research Initiative (GoMRI) provided unprecedented opportunities to study transport processes along the river-estuary-shelf-ocean continuum in the GoM. The understanding of transport at multiple spatial and temporal scales in this topographically and dynamically complex marginal sea was improved, allowing for more accurate forecasting of the fate of oil and other constituents. For this review, we focus on five specific transport themes: (i) wetland, estuary, and shelf exchanges; (ii) river-estuary coupling; (iii) nearshore and inlet processes; (iv) open ocean transport processes; and (v) river-induced fronts and cross-basin transport. We then discuss the relevancy of GoMRI findings on the transport processes for ecological connectivity and oil transport and fate. We also examine the implications of new findings for informing the response to future oil spills, and the management of coastal resources and ecosystems. Lastly, we summarize the research gaps identified in the many studies and offer recommendations for continuing the momentum of the research provided by the GoMRI effort. A number of uncertainties were identified that occurred in multiple settings. These include the quantification of sediment, carbon, dissolved gasses and nutrient fluxes during storms, consistent specification of the various external forcings used in analyses, methods for smooth integration of multiscale advection mechanisms across different flow regimes, dynamic coupling of the atmosphere with sub-mesoscale and mesoscale phenomena, and methods for simulating finer-scale dynamics over long time periods. Addressing these uncertainties would allow the scientific community to be better prepared to predict the fate of hydrocarbons and their impacts to the coastal ocean, rivers, and marshes in the event of another spill in the GoM.

Pelagic Sargassum is a buoyant macroalgae that forms rafts at the ocean surface and serves as a biologically rich habitat for hundreds of diverse marine species. Since 2011, massive blooms of Sarga...

Satellite ocean color missions require accurate system vicarious calibrations (SVC) to retrieve the relatively small remote-sensing reflectance ( $R_{\mathrm {rs}}$ , sr−1) from the at-sensor radiance. However, the current atmospheric correction and SVC procedures do not include calibration of the “long” near infrared band (NIRL—869 nm for MODIS), partially because earlier studies, based primarily on simulations, indicate that accuracy in the retrieved $R_{\mathrm {rs}}$ is insensitive to moderate changes in the NIRL vicarious gain ( $g$ ). However, the sensitivity of ocean color data products to $g$ (NIRL) has not been thoroughly examined. Here, we first derive 10 SVC “gain configurations” (vicarious gains for all visible and NIR bands) for MODIS/Aqua using current operational NASA protocols, each time assuming a different $g$ (869). From these, we derive a suite of ~1.4E6 unique gain configurations with $g$ (869) ranging from 0.85 to 1.2. All MODIS/A data for 25 locations within each of five ocean gyres were then processed using each of these gain configurations. Resultant time series show substantial variability in dominant $R_{\mathrm {rs}}$ (547) patterns in response to changes in $g$ (869) (and associated gain configurations). Overall, mean $R_{\mathrm {rs}}$ (547) values generally decrease with increasing $g$ (869), while the standard deviations around those means show gyre-specific minima for $0.97 (869) $g$ (869) using such time series, finding $g$ (869) = 1.025 most closely comports with expectations. This approach is broadly applicable to other ocean color sensors, and highlights the importance of rigorous cross-sensor calibration of the NIRL bands, with implications on consistency of merged-sensor data sets.

Over the last several decades, multiple environmental issues have led to dramatic changes in the water clarity of the Great Lakes. While many of the key factors are well-known and have direct anthropogenic origins, climatic variability and change can also impact water clarity at various temporal scales, but their influence is less often studied. Building upon a recent examination of the univariate relationships between synoptic-scale weather patterns and water clarity, this research utilizes nonlinear autoregressive models with exogenous input (NARX models) to explore the multivariate climate-to-water clarity relationship. Models trained on the observation period (1997–2016) are extrapolated back to 1979 to reconstruct a daily-scale historical water clarity dataset, and used in a reforecast mode to estimate real-time forecast skill. Of the 20 regions examined, models perform best in Lakes Michigan and Huron, especially in spring and summer. The NARX models perform better than a simple persistence model and a seasonal-trend model in nearly all regions, indicating that climate variability is a contributing factor to fluctuations in water clarity. Further, six of the 20 regions also show promise of useful forecasts to at least 1 week of lead-time, with three of those regions showing skill out to two months of lead time.

A historical water clarity index (Kd index or KDI) was developed through the use of satellite-derived and validated diffuse light attenuation (Kd; m−1) for each of the Great Lakes (and subbasins) on a daily level from 1998 to 2015. A statistical regionalization was performed with monthly level KDI using k-means clustering to subdivide the Great Lakes into regions with similar temporal variability in water clarity. The KDI was then used to assess the relationship between water clarity and atmospheric circulation patterns and stream discharge. An artificial neural-network-based self-organized map data reduction technique was used to classify atmospheric patterns using four atmospheric variables: mean sea level pressure, 500-hPa geopotential heights, zonal and meridional components of the wind at 10 m, and 850-hPa temperature. Stream discharge was found to have the strongest relationship with KDI, suggesting that sediments and dissolved matter from land runoffs are the key factors linking the atmosphere to water clarity in the Great Lakes. Although generally lower in magnitude than stream discharge, atmospheric circulation patterns associated with increased precipitation tended to have stronger positive correlations with KDI. With no long-range forecasts of stream discharge, the strong relationship between atmospheric circulation patterns and stream discharge may provide an avenue to more accurately model water clarity on a subseasonal-to-seasonal time scale.